Detaillierter Kursinhalt
Topic 1 – Analytics Workflow
- Define terms related to analytics and data science
- Describe the analytics workflow
- Describe common usage scenarios
- Navigate Splunk Machine Learning Toolkit
Topic 2 – Exploratory Data Analysis
- Describe the purpose of data exploration
- Identify SPL commands for data exploration
- Split data for testing and training using the sample command
Topic 3 – Predict Numeric Fields with Regression
- Differentiate predictions from estimates
- Identify prediction algorithms and assumptions
- Describe the fit and apply commands
- Model numeric predictions in the MLTK and Splunk Enterprise
- Use the score command to evaluate models
Topic 4 – Clean and Preprocess the Data
- Define preprocessing and describe its purpose
- Describe algorithms that preprocess data for use in models
- Choose relevant fields
- Reduce dimensionality
- Normalize data
- Preprocess text
Topic 5 – Cluster Data
- Define Clustering
- Identify clustering methods, algorithms, and use cases
- Use Smart Clustering Assistant to cluster data
- Evaluate clusters using silhouette score
- Validate cluster coherence
- Describe clustering best practices
Topic 6 – Anomaly Detection
- Define anomaly detection and outliers
- Identify anomaly detection use cases
- Use Splunk Machine Learning ToolKit Smart Outlier Assistant
- Detect anomalies using the Density Function algorithm
- Optimize anomaly detection with Local Outlier Factor
- View results with the Distribution Plot visualization
Topic 7 – Estimation and Prediction
- Differentiate predictions from forecasts
- Use the Smart Forecasting Assistant
- Use the StateSpaceForecast algorithm
- Forecast multivariate data
- Account for periodicity in each time series
Topic 8 – Classification
- Define key classification terms
- Use classification algorithms
- Evaluate classifier tradeoffs
- Evaluate results of multiple algorithms